Machine learning regression models for prediction of multiple ionospheric parameters

The variation of the ionospheric parameters has a crucial role in space weather, communication, and navigation applications. In this research, we analyze the prediction performance of three machine learning (ML) regression models, decision trees, random forest and support vector machine algorithms for the F2-layer critical frequency (f0F2), F2-layer height of the peak electron density (hmF2), and total electron content (TEC). The hourly f0F2 and hmF2 values of ROME (RO041) digisonde and hourly TEC values of a close by International GNSS Service (IGS) station (site code M0SE00ITA) were obtained for the period between January 1, 2012 and 31 December 2013. The inputs to be trained in the proposed methods are the observation periods of the data (sine and cosine of the day of year and hour of the day), hourly values of solar index F10.7 and geomagnetic index Ap, the present values of f0F2(t), hmF2 (t), TEC(t), and their values at t -23 h. The outputs are the predicted values of f0F2, hmF2, TEC at t + 1. The 2012 values of these parameters were used to train the models and they were predicted 1 h in advance during 2013. The root mean square error (RMSE) values between observed and predicted data were compared for the whole year, summer, winter, and equinox periods. The results showed that proposed ML regression models were successful in the prediction of multiple ionospheric parameters, but random forest regression provides more accurate prediction results than support vector machine and decision trees. (C) 2021 COSPAR. Published by Elsevier B.V. All rights reserved.